• Journal of Fashion Business
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• v.6 no.1
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• pp.137-154
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• 2002

It's not easy to renew old brand of over 50 years history to the tastes of new consumer of our time. Most of national brands that has a history of some 20 years in Korea have strove for continuation and growth of brand to no avails, which can be taken as a good example of current situation. For instance, C company, one of the National brand of US which has a history of 51 years, has made its position secure as a fashion group and based itself on a sound foundation by establishing new marketing strategy and completing successful brand renewal in the process of strategic M&A with Italian company. Those successful marketing strategies are as follows. 1) they regarded both market and consumer oriented marketing activity as company's highest priority strategy and put great emphasis upon concentration on target market and reestablishment of brand image of business casual wear. 2) Setting up and operating planning team composed of merchandizer alone in Milano, they set the direction of plan on the basis of concentrated research on potential item in market according to thorough market research done by buying office in Korea, branch office in Hong Kong and buyer in US prior to blueprint planning for season. 3) Great emphasis was placed on business which focused on intensive presentation of basic key item for apparel career women who are main consumer group in the midium-low prices market in US and on supplementation of size and color. they named this line 'collectibles' and helped their customer develop their own clothes plan without worrying about the change of color and fabric by supporting same fabric and color throughout the year and enabled them to add variation easily by supplementing new trend item. 4) Company set black as a main color that lots of apparel career women find easy to care and to express their own image and presented them with pebble which belongs to navy and beige and added fashion color such as wine and brown etc as season goes by. They constructed basic line in order for their customers to coordinate purchased item with new one or to add them to present collection, and to achieve efficient sale by setting up strategy which allows this cross coordination and changing pattern occasionally. 5) Though basic jacket for 99$, short slim skirt for 49$ are products within midium-low prices range, in the material planning stage aiming at production of item that has both resonable function appealing to consumer and is fashionable, synthetic material had to be used as a main source due to price competitiveness. Despite this situation, considering comfortable sense of fit and refined drape of silhouette that has no sign of cheap material, whole collectible line was divided into two items, which contributed to reduction of cost. In case of material that is composed of triacetate and polyester in 70 to 30 ratio, was used up to 4 million yard, which allowed drastic curtailment of cost accompanied by concentration. In case of 'collectibles' line, using Korean material mainly, C company chose to have their product sewed in Southeast Asian countries where transportation is well developed and both productivity and quality verified by operating global production system which aiming at cutdown of cost through outsourcing production from the country where labor cost is low and getting finished product. Polarization between present consumers telling us that consumers with the mind of middle classes in the past no longer exists between consumers who seek after only fine article of highest quality and wise consumers who are sensible enough to judge bubble on correlation between price and quality. To cope with this change in new consumer mind, apparel makes changing their policy so as to produce item that has reasonable quality and falls within affordable price range anywhere in the world. and they're striving to get out of difficult situation by operating global marketing strategy which stresses separation of planning, production and sale and sensibility of fashion shared worldwide. The marketing strategy of C company can be exemplified as a successful one.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.1
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• pp.17-25
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• 2022

This study was carried out the estimation on consumption patterns and consciousness of domestic forage for improvement of the quality of domestic forage. Although the cultivated area in South Korea of forage has increased significantly compared to the past, the self-sufficiency rate of domestic forage has increased to around 80% since 2010. Also, livestock farmers prefer to use import forage than domestic due to convenience of use. In Korean beef farms, the ratio of import to domestic forage was higher in domestic forage (import forage 3 : domestic forage 7). In the method of securing domestic forage, purchase of forage (55.6%) was higher than self-cultivation of forage (44.4%). The ratio of use by bailing type was shown in the order of rice staw rice straw (50.5%), domestic hay (15%), imported hay (12.5%), and total mixed ratio (10.7%). The preference of forage was in the order of amount of foreign matter, moisture content, price, feed value in Korean native cattle farm. The result of satisfaction with domestic and import forage showed that the satisfaction of domestic forage price was higher than import forage, while the moisture content and foreign matter of forage were lower than import forage. In addition, in the results of the satisfaction and importance of domestic roughage compared to imported roughage, satisfaction with imported roughage was generally high in all items except for price. As a result, in order to improve the satisfaction of domestic forage in Korean native cattle farm, it is necessary to minimize foreign matter in forage and increase hay production for moisture content uniform in forage.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of agricultural medicine and community health
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• v.27 no.2
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• pp.75-86
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• 2002

This study was conducted to investigate the changes in patients and medical services before and after the Separation of Prescription and Dispensing in Health Center. For the purpose of this study, prescription data of 5,890 prescribed patients in March 2000(before the Separation of Prescription and Dispensing) and 3,496 prescribed patients in March 2001(after the Separation) in 4 Health Centers located in Gyeongsangbuk-do and Gyeongsangnam-do were collected. For investigation of the change of character of prescribed patients and the disease, sex, age, chief diagnosis, the hind of medical insurance, days of visit, days of prescription were investigated by using National Health Insurance claim data. And for investigation of change of prescription, prescribed drugs per each claim, the use rate of antibiotics, injection, and high-price antiphlogistic drug were investigated for acute respiratory disease and musculoskeletal disease. The major results were as follows: For the changes of prescribed patients of each disease, patients with acute respiratory disease were decreased by 49.7% after the Separation of Prescription and Dispensing than before the Separation of Prescription and Dispensing and patients with hypertension(18.1%), patients with musculoskeletal disease(70.5%), patients with diabetes(8.5%), patients with digestive organ disease(71.2%), patients with chronic respiratory disease(76.4%) were decreased. But patients with urethritis were increased by 66.7%. The mean Health Center visited days of prescribed patients decreased significantly after the Separation of Prescription and Dispensing than before in both male and female(p<0.01) and in health insurance patients(p<0.01). For the each of the disease, hypertension, diabetes, musculoskeletal disease decreased. The mean prescribed days increased after the Separation of Prescription and Dispensing than before(p<0.01). According to the kine of disease, the mean prescribed days increased after the Separation of Prescription and Dispensing than before in all the diseases except the urethritis(p<0.01). For acute respiratory diseases, number of prescribed drugs per each claim decreased significantly after the Separation of Prescription and Dispensing(4.7 drugs) than before(4.9 drugs) and the prescription rate of injection decreased significantly from 63.8% to 7.70%, and the prescription rate of antibiotics decreased significantly from 337% to 19.1%(p<0.01). For musculoskeletal diseases before and after Separation of Prescription and Dispensing, number of prescribed drugs per each claim decreased significantly from 3.7 to 3.2 and the prescription rate of injection decreased significantly from 64.9% to 1.7%, and the prescription rate of high-price antiphlogistic drugs increased significantly from 29.1% to 397%(p<0.01). In consideration of above findings, the mean visited days decreased and on the contrary, the mean prescribed days per each prescription increased after Separation of Prescription and Dispensing than before in health centers. For the prescription pattern of physicians, number of prescribed drugs and the prescription rates of injection and antibiotics per each claim decreased, but the prescription rate of high-price antiphlogistic drugs increased after Separation of Prescription and Dispensing.

• Journal of Nutrition and Health
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• v.8 no.1
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• pp.15-37
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• 1975

I. Subject of the study A study on the nutritive value and utilization of powdered seaweeds. II. Purpose and Importance of the study A. In Korea the shortage of food will be inevitable by the rapidly growing population. It will be very important study to develop a new food from the seaweeds which were not used hitherto for human consumption. B. The several kinds of seaweeds have been used by man in Korea mainly as side-dishes. However, a properly powdered seaweed will enable itself to be a good supplement or mixture to certain cereal flours. C. By adding the powdered seaweed to any cereals which have long been staple foods in this country the two fold benefits; saving of cereals and change of dietary pattern, will be secured. III. Objects and scope of the study A. Objects of the study The objects will come under four items. 1. To develop a powdered seaweed as a new food from the seaweeds which have been not used for human consumption. 2. To evaluate the nutritional quality of the products the analysis for chemical composition and animal feeding experiment will be conducted. 3. Experimental cocking and accepability test will be conducted for the powdered products to evaluate the value as food stuff. 4. Sanitary test and also economical analysis will be conducted for the powdered products. B. Scope of the study 1. Production of seaweed powders Sargassum fulvellum growing in eastern coast and Sargassum patens C.A. in southern coast were used as the material for the powders. These algae, which have been not used for human consumption, were pulverized through the processes of washing, drying, pulverization, etc. 2. Nutritional experiments a. Chemical composition Proximate components (water, protein, fat, cellulose, sugar, ash, salt), minerals (calcium, phosphorus, iron, iodine), vitamins (A, $B_1,\;B_2$ niacin, C) and amino acids were analyzed for the seaweed powders. b. Animal feeding experiment Weaning 160 rats (80 male and 80 female rats) were used as experimental animals, dividing them into 16 groups, 10 rats each group. Each group was fed for 12 weeks on cereal diet (Wheat flour, rice powder, barley powder, potato powder, corn flour) with the supplementary levels of 5%, 10%, 15%, 20% and 30% of the seaweed powder. After the feeding the growth, feed efficiency ratio, protain efficiency ratio and ,organs weights were checked and urine analysis, feces analysis and serum analysis were also conducted. 3. Experimental cooking and acceptability test a. Several basic studies were conducted to find the characteristics of the seaweed powder. b. 17 kinds of Korean dishes and 9 kinds of foreign dishes were prepared with cereal flours (wheat, rice, barley, potato, corn) with the supplementary levels of 5%, 10%, 15%, 20% and 30% of the seaweed powder. c. Acceptability test for the dishes was conducted according to plank's Form. 4. Sanitary test The heavy metals (Cd, Pb, As, Hg) in the seaweed powders were determined. 5. Economical analysis The retail price of the seaweed powder was compared with those of other cereals in the market. And also economical analysis was made from the nutritional point of view, calculating the body weight gained in grams per unit price of each feeding diet. IV. Results of the study and the suggestion for application A. Chemical composition 1. There is no any big difference in proximate components between powders of Sargassum fulvellum in eastern coast and Sargassum patens C.A. in southern coast. Seasonal difference is also not significant. Higher levels of protein, cellulose, ash and salt were found in the powders compared with common cereal foods. 2. The levels of calcium (Ca) and iron (Fe) in the powders were significantly higher than common cereal foods and also rich in iodine (I). Existence of vitamin A and vitamin C in the Powders is different point from cereal foods. Vitamin $B_1\;and\;B_2$ are also relatively rich in the powders.'Vitamin A in ·Sargassum fulvellum is high and the levels of some minerals and vitamins are seemed4 to be some influenced by seasons. 3. In the amino acid composition methionine, isoleucine, Iysine and valine are limiting amino acids. The protein qualities of Sargassum fulvellum and Sargassum patens C.A. are seemed to be .almost same and generally ·good. Seasonal difference in amino acid composition was found. B. Animal feeding experiment 1. The best growth was found at.10% supplemental level of the seaweed Powder and lower growth rate was shown at 30% level. 2. It was shown that 15% supplemental level of the Seaweed powder seems to fulfil, to some extent the mineral requirement of the animals. 3. No any changes were found in organs development except that, in kidney, there found decreasing in weight by increasing the supplemental level of the seaweed powder. 4. There is no any significant changes in nitrogen retention, serum cholesterol, serum calcium and urinary calcium in each supplemental level of the seaweed powder. 5. In animal feeding experiment it was concluded that $5%{\sim}15%$ levels supplementation of the seaweed powder are possible. C. Experimental cooking and acceptability test 1. The seaweed powder showed to be utilized more excellently in foreign cookings than in Korean cookings. Higher supplemental level of seaweed was passible in foreign cookings. 2. Hae-Jo-Kang and Jeon-Byung were more excellent than Song-Pyun, wheat cake, Soo-Je-Bee and wheat noodle. Hae-Je-Kang was excellent in its quality even as high as 5% supplemental level. 3. The higher levels of supplementation were used the more sticky cooking products were obtained. Song-Pyun and wheat cake were palatable and lustrous in 2% supplementation level. 4. In drop cookie the higher levels of supplementation, the more crisp product was obtained, compared with other cookies. 5. Corn cake, thin rice gruel, rice gruel and potato Jeon-Byung were more excellent in their quality than potato Man-Doo and potato noodle. Corn cake, thin rice gruel and rice gruel were excellent even as high as 5% supplementation level. 6. In several cooking Porducts some seaweed-oder was perceived in case of 3% or more levels of supplementation. This may be much diminished by the use of proper condiments. D. Sanitary test It seems that there is no any heavy metals (Cd, Pb, As, Hg) problem in these seaweed Powders in case these Powders are used as supplements to any cereal flours E. Economical analysis The price of the seaweed powder is lower than those of other cereals and that may be more lowered when mass production of the seaweed powder is made in future. The supplement of the seaweed powder to any cereals is also economical with the criterion of animal growth rate. F. It is recommended that these seaweed powders should be developed and used as supplement to any cereal flours or used as other food material. By doing so, both saving of cereals and improvement of individual's nutrition will greatly be achieved. It is also recommended that the feeding experiment for men would be conducted in future.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.

• KDI Journal of Economic Policy
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• v.12 no.2
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• pp.51-79
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• 1990

Research on technical efficiency, an important dimension of market performance, had received little attention until recently by most industrial organization empiricists, the reason being that traditional microeconomic theory simply assumed away any form of inefficiency in production. Recently, however, an increasing number of research efforts have been conducted to answer questions such as: To what extent do technical ineffciencies exist in the production activities of firms and plants? What are the factors accounting for the level of inefficiency found and those explaining the interindustry difference in technical inefficiency? Are there any significant international differences in the levels of technical efficiency and, if so, how can we reconcile these results with the observed pattern of international trade, etc? As the first in a series of studies on the technical efficiency of Korea's manufacturing industries, this paper attempts to answer some of these questions. Since the estimation of technical efficiency requires the use of plant-level data for each of the five-digit KSIC industries available from the Census of Manufactures, one may consture the findings of this paper as empirical evidence of technical efficiency in Korea's manufacturing industries at the most disaggregated level. We start by clarifying the relationship among the various concepts of efficiency-allocative effciency, factor-price efficiency, technical efficiency, Leibenstein's X-efficiency, and scale efficiency. It then becomes clear that unless certain ceteris paribus assumptions are satisfied, our estimates of technical inefficiency are in fact related to factor price inefficiency as well. The empirical model employed is, what is called, a stochastic frontier production function which divides the stochastic term into two different components-one with a symmetric distribution for pure white noise and the other for technical inefficiency with an asymmetric distribution. A translog production function is assumed for the functional relationship between inputs and output, and was estimated by the corrected ordinary least squares method. The second and third sample moments of the regression residuals are then used to yield estimates of four different types of measures for technical (in) efficiency. The entire range of manufacturing industries can be divided into two groups, depending on whether or not the distribution of estimated regression residuals allows a successful estimation of technical efficiency. The regression equation employing value added as the dependent variable gives a greater number of "successful" industries than the one using gross output. The correlation among estimates of the different measures of efficiency appears to be high, while the estimates of efficiency based on different regression equations seem almost uncorrelated. Thus, in the subsequent analysis of the determinants of interindustry variations in technical efficiency, the choice of the regression equation in the previous stage will affect the outcome significantly.

• Journal of KIISE:Software and Applications
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• v.28 no.2
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• pp.179-191
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• 2001

Comparison shopping is a merchant brokering process that finds the best price for the desired product from several Web-based online stores. To get a scalable comparison shopper, we need an agent that automatically constructs a simple information extraction procedure, called a wrapper, for each semi-structured store. Automatic construction of wrappers for HTML-based Web stores is difficult because HTML only defines how information is to be displayed, not what it means, and different stores employ different ways of manipulating customer queries and different presentation formats for displaying product descriptions. Wrapper induction has been suggested as a promising strategy for overcoming this heterogeneity. However, previous scalable comparison-shoppers such as ShopBot rely on a strong bias in the product descriptions, and as a result, many stores that do not confirm to this bias were unable to be recognized. This paper proposes a more scalable comparison-shopping agent named MORPHEUS. MORPHEUS presents a simple but robust inductive learning algorithm that antomatically constructs wrappers. The main idea of the proposed algorithm is to recognize the position and the structure of a product description unit by finding the most frequent pattern from the sequence of logical line information in output HTML pages. MORPHEUS successfully constructs correct wtappers for most stores by weakening a bias assumed in previous systems. It also tolerates some noises that might be present in production descriptions such as missing attributes. MORPHEUS generates the wrappers rapidly by excluding the pre-processing phase of removing redundant fragments in a page such as a header, a tailer, and advertisements. Eventually, MORPHEUS provides a framework from which a customized comparison-shopping agent can be organized for a user by facilitating the dynamic addition of new stores.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1517-1524
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• 2014

In recent years, the interest of environmental-friendly transportation modes has been growing. This is because of social and environmental problems such as increasing gas price and climate change. In Europe, bike-sharing service, one of the environmental-friendly transportation modes, has been already operated. Bike-sharing service named "Tashu" has been operated in Daejeon city since 2009. This study is a fundamental research to increase utilization efficiency of bike-sharing service and to decide optimal locations of bike stations. In addition this study examines characteristics of bike usage and analyzes factors affecting to demands using multiple regression model. Based on the result of examining of characteristics of bike usage, the rate of bike usage is higher compared with installation rate of public bike stations near parks in Daejeon. In addition demands of bike usage in weekend is higher than in weekday. It reveals that the main purpose of bike usage could be recreational activities. The return rate at the same location with rental station is comparatively high. Moreover, bike usage pattern is biased in specific areas (Dunsan and Yuseong) because bike-sharing stations are not equally located. As a result of multiple regression model, the factors affecting to demands are number of passengers in buses, length of bike lanes, parks, distance to waterfront, and rate of young people. A statistical significance of factors (r-square) is 0.748, which has strong relationship.